Novel Contributions of the Smoothelin‐like 1 Protein in Vascular Smooth Muscle Contraction and its Potential Involvement in Myogenic Tone
Bibliographic record
Abstract
Vascular smooth muscle contraction and the myogenic response regulate blood flow in the resistance vascular and contribute to systemic blood pressure. Three pathways are currently known to contribute to the development of the myogenic response: (i) Ca(2+) -dependent phosphorylation of LC20; (ii) Ca(2+) sensitization through inhibition of myosin phosphatase; and (iii) cortical actin polymerization. A number of regulatory smooth muscle proteins are integrated with these pathways to fine tune the response and facilitate adaptations to vascular (patho)physiologies. Of particular interest is the SMTN family of proteins, consisting of SMTN-A, SMTN-B, and the SMTN-like protein, SMTNL1. The SMTN-B and SMTNL1 proteins are both implicated in regulating smooth muscle contractility and contributing to vascular adaptations associated with hypertension, pregnancy, and exercise training. In the case of SMTNL1, the protein plays multiple roles in regulating contraction through functional interactions with contractile regulators as well as transcriptional control of the contractile phenotype and Ca(2+) -sensitizing capacity. For the first time, preliminary results suggest SMTNL1 is involved in the myogenic response of the cerebral resistance vasculature. In this regard, global SMTNL1 deletion is associated with greater myogenic reactivity of cerebral arterioles, although the precise mechanism accounting for this finding remains to be defined.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".